{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01b24fcd-f32a-461d-a0fd-ef942da9ca80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
      "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
      "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
      "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
      "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222.0   \n",
      "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222.0   \n",
      "\n",
      "   PTRATIO       B  LSTAT  MEDV  \n",
      "0     15.3  396.90   4.98  24.0  \n",
      "1     17.8  396.90   9.14  21.6  \n",
      "2     17.8  392.83   4.03  34.7  \n",
      "3     18.7  394.63   2.94  33.4  \n",
      "4     18.7  396.90   5.33  36.2  \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#指定列名\n",
    "column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\n",
    "\n",
    "# 读取数据\n",
    "data=pd.read_csv(r'E:\\JupyterProjects\\ML-assignment1-多元线性回归\\housing_data.txt', sep=r'\\s+', header=None, names=column_names)\n",
    "print(data.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aaa29daf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    int64  \n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    float64\n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据集信息\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d60226d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对所有数据类型字段进行统计分析，可以初步识别异常数据\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e56ac8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
      "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
      "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
      "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
      "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222.0   \n",
      "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222.0   \n",
      "\n",
      "   PTRATIO       B  LSTAT  MEDV  \n",
      "0     15.3  396.90   4.98  24.0  \n",
      "1     17.8  396.90   9.14  21.6  \n",
      "2     17.8  392.83   4.03  34.7  \n",
      "3     18.7  394.63   2.94  33.4  \n",
      "4     18.7  396.90   5.33  36.2  \n",
      "        CRIM   ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
      "450  6.71772  0.0   18.1     0  0.713  6.749  92.6  2.3236   24  666.0   \n",
      "451  5.44114  0.0   18.1     0  0.713  6.655  98.2  2.3552   24  666.0   \n",
      "452  5.09017  0.0   18.1     0  0.713  6.297  91.8  2.3682   24  666.0   \n",
      "453  8.24809  0.0   18.1     0  0.713  7.393  99.3  2.4527   24  666.0   \n",
      "454  9.51363  0.0   18.1     0  0.713  6.728  94.1  2.4961   24  666.0   \n",
      "\n",
      "     PTRATIO       B  LSTAT  MEDV  \n",
      "450     20.2    0.32  17.44  13.4  \n",
      "451     20.2  355.29  17.73  15.2  \n",
      "452     20.2  385.09  17.27  16.1  \n",
      "453     20.2  375.87  16.74  17.8  \n",
      "454     20.2    6.68  18.71  14.9  \n"
     ]
    }
   ],
   "source": [
    "#拆分数据集\n",
    "train_data = data.iloc[:450]\n",
    "test_data = data.iloc[450:]\n",
    "\n",
    "# 查看训练集和测试集\n",
    "print(train_data.head())\n",
    "print(test_data.head())\n",
    "#从此开始，我们将使用train_data作为训练集，test_data作为测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b61c118",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制测试集直方图\n",
    "import matplotlib.pyplot as plt\n",
    "train_data.hist(bins=20, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5515ef93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.3200e-03 1.8000e+01 2.3100e+00 0.0000e+00 5.3800e-01 6.5750e+00\n",
      "  6.5200e+01 4.0900e+00 1.0000e+00 2.9600e+02 1.5300e+01 3.9690e+02\n",
      "  4.9800e+00]\n",
      " [2.7310e-02 0.0000e+00 7.0700e+00 0.0000e+00 4.6900e-01 6.4210e+00\n",
      "  7.8900e+01 4.9671e+00 2.0000e+00 2.4200e+02 1.7800e+01 3.9690e+02\n",
      "  9.1400e+00]\n",
      " [2.7290e-02 0.0000e+00 7.0700e+00 0.0000e+00 4.6900e-01 7.1850e+00\n",
      "  6.1100e+01 4.9671e+00 2.0000e+00 2.4200e+02 1.7800e+01 3.9283e+02\n",
      "  4.0300e+00]\n",
      " [3.2370e-02 0.0000e+00 2.1800e+00 0.0000e+00 4.5800e-01 6.9980e+00\n",
      "  4.5800e+01 6.0622e+00 3.0000e+00 2.2200e+02 1.8700e+01 3.9463e+02\n",
      "  2.9400e+00]\n",
      " [6.9050e-02 0.0000e+00 2.1800e+00 0.0000e+00 4.5800e-01 7.1470e+00\n",
      "  5.4200e+01 6.0622e+00 3.0000e+00 2.2200e+02 1.8700e+01 3.9690e+02\n",
      "  5.3300e+00]]\n",
      "[24.  21.6 34.7 33.4 36.2]\n"
     ]
    }
   ],
   "source": [
    "# 提取特征和目标变量\n",
    "X_train = train_data.drop('MEDV', axis=1).values\n",
    "y_train = train_data['MEDV'].values\n",
    "\n",
    "X_test = test_data.drop('MEDV', axis=1).values\n",
    "y_test = test_data['MEDV'].values\n",
    "\n",
    "# 查看特征和目标变量\n",
    "print(X_train[:5])\n",
    "print(y_train[:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43e17c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算训练集特征的均值和标准差\n",
    "mean = X_train.mean(axis=0)\n",
    "std = X_train.std(axis=0)\n",
    "\n",
    "# 标准化训练集和测试集的特征\n",
    "X_train = (X_train - mean) / std\n",
    "X_test = (X_test - mean) / std\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc4a987d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0000e+00 6.3200e-03 1.8000e+01 2.3100e+00 0.0000e+00 5.3800e-01\n",
      "  6.5750e+00 6.5200e+01 4.0900e+00 1.0000e+00 2.9600e+02 1.5300e+01\n",
      "  3.9690e+02 4.9800e+00]\n",
      " [1.0000e+00 2.7310e-02 0.0000e+00 7.0700e+00 0.0000e+00 4.6900e-01\n",
      "  6.4210e+00 7.8900e+01 4.9671e+00 2.0000e+00 2.4200e+02 1.7800e+01\n",
      "  3.9690e+02 9.1400e+00]\n",
      " [1.0000e+00 2.7290e-02 0.0000e+00 7.0700e+00 0.0000e+00 4.6900e-01\n",
      "  7.1850e+00 6.1100e+01 4.9671e+00 2.0000e+00 2.4200e+02 1.7800e+01\n",
      "  3.9283e+02 4.0300e+00]\n",
      " [1.0000e+00 3.2370e-02 0.0000e+00 2.1800e+00 0.0000e+00 4.5800e-01\n",
      "  6.9980e+00 4.5800e+01 6.0622e+00 3.0000e+00 2.2200e+02 1.8700e+01\n",
      "  3.9463e+02 2.9400e+00]\n",
      " [1.0000e+00 6.9050e-02 0.0000e+00 2.1800e+00 0.0000e+00 4.5800e-01\n",
      "  7.1470e+00 5.4200e+01 6.0622e+00 3.0000e+00 2.2200e+02 1.8700e+01\n",
      "  3.9690e+02 5.3300e+00]]\n",
      "[[1.00000e+00 6.71772e+00 0.00000e+00 1.81000e+01 0.00000e+00 7.13000e-01\n",
      "  6.74900e+00 9.26000e+01 2.32360e+00 2.40000e+01 6.66000e+02 2.02000e+01\n",
      "  3.20000e-01 1.74400e+01]\n",
      " [1.00000e+00 5.44114e+00 0.00000e+00 1.81000e+01 0.00000e+00 7.13000e-01\n",
      "  6.65500e+00 9.82000e+01 2.35520e+00 2.40000e+01 6.66000e+02 2.02000e+01\n",
      "  3.55290e+02 1.77300e+01]\n",
      " [1.00000e+00 5.09017e+00 0.00000e+00 1.81000e+01 0.00000e+00 7.13000e-01\n",
      "  6.29700e+00 9.18000e+01 2.36820e+00 2.40000e+01 6.66000e+02 2.02000e+01\n",
      "  3.85090e+02 1.72700e+01]\n",
      " [1.00000e+00 8.24809e+00 0.00000e+00 1.81000e+01 0.00000e+00 7.13000e-01\n",
      "  7.39300e+00 9.93000e+01 2.45270e+00 2.40000e+01 6.66000e+02 2.02000e+01\n",
      "  3.75870e+02 1.67400e+01]\n",
      " [1.00000e+00 9.51363e+00 0.00000e+00 1.81000e+01 0.00000e+00 7.13000e-01\n",
      "  6.72800e+00 9.41000e+01 2.49610e+00 2.40000e+01 6.66000e+02 2.02000e+01\n",
      "  6.68000e+00 1.87100e+01]]\n"
     ]
    }
   ],
   "source": [
    "# 添加一列全为1的特征 \n",
    "X_train_b = np.c_[np.ones((X_train.shape[0], 1)), X_train]\n",
    "X_test_b = np.c_[np.ones((X_test.shape[0], 1)), X_test]\n",
    "\n",
    "print(X_train_b[:5])\n",
    "print(X_test_b[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4fd397e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.42612251e+01 -1.12706397e-01  4.84679322e-02  3.51619982e-02\n",
      "  2.43036682e+00 -1.71555914e+01  3.92937664e+00  1.05952560e-02\n",
      " -1.40598405e+00  3.72316544e-01 -1.54340774e-02 -9.03360601e-01\n",
      "  9.74047349e-03 -5.51630479e-01]\n"
     ]
    }
   ],
   "source": [
    "#多元线性回归模型\n",
    "\n",
    "# 使用方程来拟合模型参数\n",
    "beta = np.linalg.inv(X_train_b.T.dot(X_train_b)).dot(X_train_b.T).dot(y_train)\n",
    "\n",
    "# 查看模型参数\n",
    "print(beta)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "796f3a3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值:  [16.93342847 20.02045317 19.1112216  23.22511636 15.67053396 16.16461608\n",
      " 12.94871109 13.10761941 17.6319346  19.03284579 19.59255106 20.77045231\n",
      " 20.32972514 23.18351143 20.68319592 18.0040013  14.64560774 17.27282806\n",
      " 17.13977735 18.68551487 20.60338383 23.55507294 22.87962378 26.04862777\n",
      " 16.72644386 16.36479094 21.00709718 11.6612751  19.64804461 22.3241981\n",
      " 23.86289088 27.72777956 29.28276029 21.24700039 19.56624682 22.57946369\n",
      " 20.10876414 21.5593847  10.86871125  7.10200759  2.34094092 12.89668156\n",
      " 15.06381197 20.29041819 20.14661342 16.17245839 13.60940848 18.92607466\n",
      " 21.08818519 18.21312845 20.37078103 23.66927765 22.53513428 28.11477396\n",
      " 26.56785292 22.58084479]\n",
      "均方误差（MSE）:  11.407003268829666\n"
     ]
    }
   ],
   "source": [
    "#利用模型参数进行预测\n",
    "\n",
    "y_predict = X_test_b.dot(beta)\n",
    "print(\"预测值: \", y_predict)\n",
    "\n",
    "# 计算均方误差\n",
    "mse = np.mean((y_test - y_predict) ** 2)\n",
    "print(\"均方误差（MSE）: \", mse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "70bb032b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#预测值与实际值的散点图\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Actual Prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title('Actual Prices vs Predicted Prices')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
